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Bias attenuation results for dichotomization of a continuous confounder

Author

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  • Gabriel Erin E.

    (Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark)

  • Peña Jose M.

    (Department of Computer and Information Science, Linköping University, Linköping, Sweden)

  • Sjölander Arvid

    (Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden)

Abstract

It is well-known that dichotomization can cause bias and loss of efficiency in estimation. One can easily construct examples where adjusting for a dichotomized confounder causes bias in causal estimation. There are additional examples in the literature where adjusting for a dichotomized confounder can be more biased than not adjusting at all. The message is clear, do not dichotomize. What is unclear is if there are scenarios where adjusting for the dichotomized confounder always leads to lower bias than not adjusting. We propose several sets of conditions that characterize scenarios where one should always adjust for the dichotomized confounder to reduce bias. We then highlight scenarios where the decision to adjust should be made more cautiously. To our knowledge, this is the first formal presentation of conditions that give information about when one should and potentially should not adjust for a dichotomized confounder.

Suggested Citation

  • Gabriel Erin E. & Peña Jose M. & Sjölander Arvid, 2022. "Bias attenuation results for dichotomization of a continuous confounder," Journal of Causal Inference, De Gruyter, vol. 10(1), pages 515-526, January.
    • Handle: RePEc:bpj:causin:v:10:y:2022:i:1:p:515-526:n:1
    DOI: 10.1515/jci-2022-0047
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    Keywords

    bias; causal inference; dichotomized confounder; 62D20;
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